Cargando…

Alignment-based Protein Mutational Landscape Prediction: Doing More with Less

The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have ov...

Descripción completa

Detalles Bibliográficos
Autores principales: Abakarova, Marina, Marquet, Céline, Rera, Michael, Rost, Burkhard, Laine, Elodie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653582/
https://www.ncbi.nlm.nih.gov/pubmed/37936309
http://dx.doi.org/10.1093/gbe/evad201
_version_ 1785136446521212928
author Abakarova, Marina
Marquet, Céline
Rera, Michael
Rost, Burkhard
Laine, Elodie
author_facet Abakarova, Marina
Marquet, Céline
Rera, Michael
Rost, Burkhard
Laine, Elodie
author_sort Abakarova, Marina
collection PubMed
description The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline.
format Online
Article
Text
id pubmed-10653582
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-106535822023-11-04 Alignment-based Protein Mutational Landscape Prediction: Doing More with Less Abakarova, Marina Marquet, Céline Rera, Michael Rost, Burkhard Laine, Elodie Genome Biol Evol Letter The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline. Oxford University Press 2023-11-04 /pmc/articles/PMC10653582/ /pubmed/37936309 http://dx.doi.org/10.1093/gbe/evad201 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Letter
Abakarova, Marina
Marquet, Céline
Rera, Michael
Rost, Burkhard
Laine, Elodie
Alignment-based Protein Mutational Landscape Prediction: Doing More with Less
title Alignment-based Protein Mutational Landscape Prediction: Doing More with Less
title_full Alignment-based Protein Mutational Landscape Prediction: Doing More with Less
title_fullStr Alignment-based Protein Mutational Landscape Prediction: Doing More with Less
title_full_unstemmed Alignment-based Protein Mutational Landscape Prediction: Doing More with Less
title_short Alignment-based Protein Mutational Landscape Prediction: Doing More with Less
title_sort alignment-based protein mutational landscape prediction: doing more with less
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653582/
https://www.ncbi.nlm.nih.gov/pubmed/37936309
http://dx.doi.org/10.1093/gbe/evad201
work_keys_str_mv AT abakarovamarina alignmentbasedproteinmutationallandscapepredictiondoingmorewithless
AT marquetceline alignmentbasedproteinmutationallandscapepredictiondoingmorewithless
AT reramichael alignmentbasedproteinmutationallandscapepredictiondoingmorewithless
AT rostburkhard alignmentbasedproteinmutationallandscapepredictiondoingmorewithless
AT laineelodie alignmentbasedproteinmutationallandscapepredictiondoingmorewithless